Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Many researchers have employed some form of teleoperated leader to influence a robotic swarm; however, the way in which this influence is conveyed has not been well studied. Some researchers employ designated leaders that are known to be leaders by other members of the swarm and hence followed. Others do not impose a leader/follower distinction on the swarm’s algorithms and instead choose to influence the swarm indirectly through controlling one or more of its members. Because the robustness of swarm behavior arises from its many distributed interactions, influence through designated leaders might render it susceptible to noise or disrupt its coherence by overriding these mechanisms. Conversely, limiting human influence to indirect control through the local effects of a leader might prove too sluggish to allow effective human control. This paper compares leader- based methods of each type, designated as consensus (no explicit leader/follower distinction) and flooding (influence propagating from leader takes precedence). Our overall methodology was to compare the two methods, Explicit influence via flooding and Tacit influence via consensus, both in simulation and in experiments with human operators. We compared the two methods for convergence time and properties in noisy and noiseless conditions with static and dynamic graphs. We found that consensus converged much slower than flooding but had slightly better noise tolerance. In the human experiments we compared the ability of operators to maneuver a swarm to goal points using each method, both with and without sensing error. Under flooding each robot matched the speed and direction of the leader (or matched the speed and direction of a neighboring robot already aligned with the leader). Under consensus, robots matched the average speed and direction of neighbors within sensor range. As in simulation, the flooding method was significantly more effective in moving the swarm between goal points. The greater sensitivity of flooding to error found in simulation, however, was not observed in the human experiments. Instead, the error degraded performance equally across the two conditions. Additionally, in the human experiments the consensus method did show advantages in improving overall connectivity and cohesion of the swarm.